Typical methods of DOA estimation of a signal using a sensor array include digital beamforming (DBF), a subspace method (SSM), and maximum likelihood (ML) estimation. The DBF method includes a Capon method and linear prediction. The SSM includes multiple signal classification (MUSIC), estimation of signal parameters via rotation invariance techniques (ESPRIT), and a propagator method. The ML estimation includes a method of direction estimation (MODE).
In these methods, the estimation precision and the computation load increases in the following order: DBF<SSM<ML. With the SSM, there is a good balance between the computation load and the estimation precision and, thus, is the practical method to be chosen. However, when it is assumed that a central processing unit (CPU) of several tens of mega hertz (MHz) is to be used, it is difficult to perform real-time processing with the MUSIC or the ESPRIT because the computation load for eigenvalue decomposition, which is the main computation process required in the SSM, is large.
On the other hand, with the propagator method or an orthonormal propagator method, which is an improved version of the propagator method, real-time processing can be performed because the main computation process is merely calculation of inverse matrices, but sufficient estimation precision cannot be achieved.